The following report demonstrates a series of plots generated using WHO data extracted from kaggle: [https://www.kaggle.com/datasets/utkarshxy/who-worldhealth-statistics-2020-complete], these excel data sets used for this consist of information based on:
-Life expectancy -Number of cases of Malaria/Turbeculosis -Road traffic deaths -Population numbers and many more.
The data used was recorded from 2000-2016 and therefore can be considered relevant.
The Choropleth graph above demonstrates to us that Africa generally demonstrated the greatest number of malaria incidences during 2010. This could be due to a number of factors including weather conditions allowing the species to thrive or perhaps due to lack of medical care available to treat the disease early. This map encouraged me to investigate further into life expectancy globally to discover whether or not this correlated with the incidences of Malaria recorded in this time period.
The interactive world map demonstrates the co-ordinates of the individual countries from the data set and the colour scheme allows us to acknowledge that Africa, New Zealand and south Asia generally demonstrates larger proportions of the population with primary reliance on clean fuels and technologies compared to the rest of the globe. We could estimate the reason for this to be due to lack of external resources for industrial production of products.
####The line chart here demonstrates a general trend of life expectancy increasing globally through the 2000’s, this increase appears to be greater from those countries in Africa compared with the Western Pacific and Europe where life expectancy was already high, indicating strong development and progression in healthcare must be prevelant in Africa.
The grouped boxplot above demonstrates to us the range in the average life expectancy of individuals across each continent from the years 2000-2019 and we are able to see that generally Africa has experienced the lowest life expectancy among individuals and we could estimate that the higher incidences of malaria cases here as opposed to other regions is a significant depending factor for this. The relationship between factors fascinated me and therefore encouraged me to investigate the correlation between other factors such as road traffic deaths, sanitation levels, doctors available and many more.
From the Interactive heat map above we are able to see which countries lack specific sanitation facilities, have the larger portion of individuals suffering from cancer, the number of road traffic deaths and the level of doctor availability. In order to see whether these individual variables showed any type of correlation between them I constructed a correlation plot where the negative values indicate negative correlation and vice versa.